Detecting missing values with isna() in Pandas - Mini Project: Build & Apply

Detecting missing values with isna()

📖 Scenario: You work as a data analyst for a small online store. You have a table of customer orders, but some information is missing. You want to find out which orders have missing data so you can fix or investigate them.

🎯 Goal: You will create a pandas DataFrame with order data, then use the isna() method to find missing values in the data.

📋 What You'll Learn

Create a pandas DataFrame with specific order data including some missing values

Create a variable to hold the result of detecting missing values using isna()

Print the DataFrame showing which values are missing

💡 Why This Matters

🌍 Real World

Detecting missing data is important in real-world data analysis to clean and prepare data for accurate results.

💼 Career

Data analysts and scientists often check for missing values to decide how to handle incomplete data before analysis.

1

Create the orders DataFrame

Import pandas as pd and create a DataFrame called orders with these exact columns and values:
'OrderID': [101, 102, 103, 104],
'Customer': ['Alice', 'Bob', None, 'David'],
'Amount': [250, None, 150, 300]

Pandas

# Import pandas and create the orders DataFrame with the given data
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary of lists for columns.

2

Create a variable to detect missing values

Create a variable called missing_values that stores the result of calling orders.isna().

Pandas

import pandas as pd

orders = pd.DataFrame({
    'OrderID': [101, 102, 103, 104],
    'Customer': ['Alice', 'Bob', None, 'David'],
    'Amount': [250, None, 150, 300]
})

# Create missing_values using orders.isna()
# Your code here

Need a hint?

Use the isna() method on the orders DataFrame.

3

Check missing values in the DataFrame

Use a print() statement to display the missing_values DataFrame.

Pandas

import pandas as pd

orders = pd.DataFrame({
    'OrderID': [101, 102, 103, 104],
    'Customer': ['Alice', 'Bob', None, 'David'],
    'Amount': [250, None, 150, 300]
})

missing_values = orders.isna()

# Print the missing_values DataFrame
# Your code here

Need a hint?

Use print(missing_values) to see the True/False table.

4

Display missing values clearly

Print the orders DataFrame and then print the missing_values DataFrame to compare the original data with missing value locations.

Pandas

import pandas as pd

orders = pd.DataFrame({
    'OrderID': [101, 102, 103, 104],
    'Customer': ['Alice', 'Bob', None, 'David'],
    'Amount': [250, None, 150, 300]
})

missing_values = orders.isna()

print(missing_values)

# Print the orders DataFrame to compare
# Your code here

Need a hint?

Print both DataFrames one after the other to see missing values clearly.